Artificial IntelligenceNCT07273890
Real-time Feedback of Red-out Within Colonoscopy Intubation
A study testing AI system in people with Artificial Intelligence and Colonoscopy, run by The First Affiliated Hospital of Anhui Medical University.
Trial summary
This study will employ a prospective, multicenter, controlled design. It will be conducted across multiple centers, with participated centers randomly assigned to one of four groups: Group A, Group B, Group C, and Group D. Read more about this study
- Who is it for?
- Ages 18 to 70 years
- Conditions the study is for
- Artificial Intelligence, Colonoscopy, Real-time Feedback
- What is being tested?
- AI system
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 576 participants
- Where
- No sites listed yet
- Study dates
- 10 Dec 2025to 20 Apr 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 70 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Study Participants (Patients):
- Aged 18 to 70 years, any gender. Individuals scheduled to undergo diagnostic or screening colonoscopy at the investigational site.
- Colonoscopists:
- Expert-level colonoscopists (having performed a total of >1000 colonoscopy procedures).
- Right-handed.
Who cannot join
- Study Participants (Patients):
- Individuals undergoing the following procedures:
- cases with a history of colorectal surgery; cases with a history of chemotherapy, raditherapy; cases with a history of abdominal, and/or pelvic surgery; cases with a history of difficult colonoscopies; cases with colorectal tumours and obstructive lesions; cases with colorectal diverticula; cases with ulcerative colitis or Crohn's disease; cases with ischemic bowel disease; cases with colorectal polyposis; cases with melanosis coli; cases undergoing sigmoidoscopy; cases with poor intestional cleanliness (segment Boston bowel preparation scale (BBPS) of < 2 points, total BBPS of < 6 points); cases undergoing therapy procedures such as biopsy or CSP during the intubation phase; cases with transparent cap assisted colonoscopy; cases with water-assisted colonoscopy; cases with air insufflation level of M or L; cases failed caecal intubation within 15 min; cases with colonoscope stiffness level > 0; obese cases or underweight cases; and cases refusing participation.
- Individuals who decline to provide informed consent.
- Colonoscopists:
- Those who have performed fewer than 300 complete colonoscopies in any calendar year within the past three years.
- Those who decline to participate in the study.
About this study
Official title: Prospective, Multicenter, Controlled Study on the Impact of Real-time Feedback on Red-out
This study will employ a prospective, multicenter, controlled design. It will be conducted across multiple centers, with participated centers randomly assigned to one of four groups: Group A, Group B, Group C, and Group D.
The research will primarily focus on the AI-based analysis of colonoscopic images to calculate the following metrics: caecal intubation time, red-out percentage, and the AI-based red-out avoiding score. Based on the study's implementation protocol, a decision will be made regarding whether to provide real-time feedback. Additionally, the presence of any complications will be assessed both during and after the colonoscopy procedure.
- Conditions
- Artificial Intelligence, Colonoscopy, Real-time Feedback
- Being tested
- AI system
- Keywords
- Colonoscopy, Intubation, Real-time feedback, Red-out
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI systemOther
AI-system Performance Feedback in group B, group C, and group D.
| Study group | Role | What they receive |
|---|---|---|
| Group A | Sham comparator | During colonoscopy intubation, AI system is used to calculate and analyze "caecal intubation time," "red-out percentage," and the "AI-based red-out avoiding score" in real-time; however, these results are not provided as feedback to the operating colonoscopist. Receives: Other: AI system |
| Group B | Experimental | During colonoscopy intubation, AI system is used to calculate and analyze "caecal intubation time," "red-out percentage," and the "AI-based red-out avoiding score" in real-time, with only the caecal intubation time being provided as feedback to the operator, while the red-out percentage and AI-based red-out avoiding score are withheld. Receives: Other: AI system |
| Group C | Experimental | During colonoscopy intubation, AI system is used to calculate and analyze "caecal intubation time," "red-out percentage," and the "AI-based red-out avoiding score" in real-time, with only the red-out percentage being provided as feedback to the operator, while the caecal intubation time and AI-based red-out avoiding score are withheld. Receives: Other: AI system |
| Group D | Experimental | During colonoscopy intubation, AI system is used to calculate and analyze the "caecal intubation time" "red-out percentage," and "AI-based red-out avoiding score" in real-time, with all three results provided as feedback to the operating colonoscopist. Receives: Other: AI system |
What is being measured
The primary outcome is the main question the study is designed to answer.
Red-out percentage
The impact of real-time feedback on red-out percentage.
Measured: Stage 3 (projected to begin in 3-6 months)
Caecal intubation time
The impact of real-time feedback on caecal intubation time.
Measured: Stage 3 (projected to begin in 3-6 months)
AI-based red-out avoiding score
The impact of real-time feedback on AI-based red-out avoiding score.
Measured: Stage 3 (projected to begin in 3-6 months)
Secondary outcomes (1)
Complications
During and after the colonoscopy, assess for any signs of complications
Measured: Stage 3 (projected to begin in 3-6 months)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Dec 2025
- Study start10 Dec 2025 (estimated)
- Primary completion31 Aug 2027 (estimated)
- Study completion20 Apr 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 576 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The First Affiliated Hospital of Anhui Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07273890
- Sponsor's protocol ID
- AHMU-Feedback of Red-out
- Status last verified
- Oct 2025
- First submitted
- 27 Nov 2025
- Last updated
- 10 Dec 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 10 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective, Multicenter, Controlled Study on the Impact of Real-time Feedback on Red-out
- ClinicalTrials.gov ID
- NCT07273890
- Acronym / sponsor ID
- AHMU-Feedback of Red-out
- Conditions
- Artificial Intelligence, Colonoscopy, Real-time Feedback
- Keywords
- Colonoscopy, Intubation, Real-time feedback, Red-out
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 576 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Anhui Medical UniversityUniversity, hospital or other
- Study start
- 10 Dec 2025 (estimated)
- Primary completion
- 31 Aug 2027 (estimated)
- Study completion
- 20 Apr 2028 (estimated)
- First posted
- 10 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study will employ a prospective, multicenter, controlled design. It will be conducted across multiple centers, with participated centers randomly assigned to one of four groups: Group A, Group B, Group C, and Group D.
The research will primarily focus on the AI-based analysis of colonoscopic images to calculate the following metrics: caecal intubation time, red-out percentage, and the AI-based red-out avoiding score. Based on the study's implementation protocol, a decision will be made regarding whether to provide real-time feedback. Additionally, the presence of any complications will be assessed both during and after the colonoscopy procedure.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Group A | Sham comparator | Other: AI systemDuring colonoscopy intubation, AI system is used to calculate and analyze "caecal intubation time," "red-out percentage," and the "AI-based red-out avoiding score" in real-time; however, these results are not provided as feedback to the operating colonoscopist. |
| Group B | Experimental | Other: AI systemDuring colonoscopy intubation, AI system is used to calculate and analyze "caecal intubation time," "red-out percentage," and the "AI-based red-out avoiding score" in real-time, with only the caecal intubation time being provided as feedback to the operator, while the red-out percentage and AI-based red-out avoiding score are withheld. |
| Group C | Experimental | Other: AI systemDuring colonoscopy intubation, AI system is used to calculate and analyze "caecal intubation time," "red-out percentage," and the "AI-based red-out avoiding score" in real-time, with only the red-out percentage being provided as feedback to the operator, while the caecal intubation time and AI-based red-out avoiding score are withheld. |
| Group D | Experimental | Other: AI systemDuring colonoscopy intubation, AI system is used to calculate and analyze the "caecal intubation time" "red-out percentage," and "AI-based red-out avoiding score" in real-time, with all three results provided as feedback to the operating colonoscopist. |
Who may qualify
- Ages
- 18 to 70 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- Study Participants (Patients):
- Aged 18 to 70 years, any gender. Individuals scheduled to undergo diagnostic or screening colonoscopy at the investigational site.
- Colonoscopists:
- Expert-level colonoscopists (having performed a total of >1000 colonoscopy procedures).
- Right-handed.
Key exclusion criteria (7 listed)
- Study Participants (Patients):
- cases with a history of colorectal surgery; cases with a history of chemotherapy, raditherapy; cases with a history of abdominal, and/or pelvic surgery; cases with a history of difficult colonoscopies; cases with colorectal tumours and obstructive lesions; cases with colorectal diverticula; cases with ulcerative colitis or Crohn's disease; cases with ischemic bowel disease; cases with colorectal polyposis; cases with melanosis coli; cases undergoing sigmoidoscopy; cases with poor intestional cleanliness (segment Boston bowel preparation scale (BBPS) of < 2 points, total BBPS of < 6 points); cases undergoing therapy procedures such as biopsy or CSP during the intubation phase; cases with transparent cap assisted colonoscopy; cases with water-assisted colonoscopy; cases with air insufflation level of M or L; cases failed caecal intubation within 15 min; cases with colonoscope stiffness level > 0; obese cases or underweight cases; and cases refusing participation.
- Individuals who decline to provide informed consent.
- Colonoscopists:
- Those who have performed fewer than 300 complete colonoscopies in any calendar year within the past three years.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI systemAI-system Performance Feedback in group B, group C, and group D. | Other | Group A, Group B, Group C, Group D |
Primary outcome
Red-out percentage
Time frame: Stage 3 (projected to begin in 3-6 months)
The impact of real-time feedback on red-out percentage.
Caecal intubation time
Time frame: Stage 3 (projected to begin in 3-6 months)
The impact of real-time feedback on caecal intubation time.
AI-based red-out avoiding score
Time frame: Stage 3 (projected to begin in 3-6 months)
The impact of real-time feedback on AI-based red-out avoiding score.
1 secondary outcome
Complications
Time frame: Stage 3 (projected to begin in 3-6 months)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Dec 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing AI system in people with Artificial Intelligence and Colonoscopy, run by The First Affiliated Hospital of Anhui Medical University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 10 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Red-out percentage (time frame: Stage 3 (projected to begin in 3-6 months)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Dec 2025.